The autonomous driving market is finally hitting its stride, thanks to wireless connectivity and a range of modern technologies that allow data to be captured, processed, and analyzed rapidly. Today’s vehicles pack hundreds of devices and components that track everything from how a car is functioning and environmental and safety conditions to driver-generated behavioral information. As much information as we all use on our mobile phones, cars will continue to generate even more.
IoT sensors, AI and machine learning technologies, and big data analysis are all at the heart of the connected car and autonomous driving phenomenon. The global automotive IoT market is expected to reach $541.73 billion by 2025, growing at a CAGR of 16.4 percent, and connected car shipments are projected to rise from 33 million in 2017 to over 77 million by 2025, according to Business Insider.
Better Autonomous Driving Safety
Most modern vehicles today include sophisticated advanced driver assistance systems, known as ADAS. These ADAS systems include an array of automated functions such as adaptive cruise control, anti-lock brakes, collision warnings, high beam safety, lane departure, traffic signal recognition, environmental and road condition monitoring, telemetry sensors to track routes and traffic patterns, and even biometric readers that can determine if a driver is falling asleep at the wheel.
Machine learning (ML) makes all of these autonomous driving activities happen almost instantaneously by processing the data from radar or cameras in real time to identify what an object is (such as a road hazard) and how to deal with it. ML decision-making can be broken down into three categories:
1. Pattern Recognition Algorithms and Classifications
ADAS captures images, filters them to rule out irrelevant data points, and applies pattern recognition algorithms to identify and classify objects it sees. It reads object edges, line segments, and circular arcs at the edges to get a full picture of the object.
In the event that an image is not fully clear, such as in low-resolution images with very few data points or discontinuous data, clustering allows ML to analyze inherent structures in the data and organize them by commonality to generate a projected image.
3. Decision Matrix Algorithms
ML systematically analyzes and rates the performance of sets of values to make an important decision, such as turning left or braking. The result is analyzed by ML as it determines a level of confidence in classification, recognition, and prediction of the next movement.
Predicting When a Car Needs Maintenance
IoT and AI-powered processes help empower accurate diagnostics and predict equipment malfunctions. By reading things like RPM, oil and engine temperatures, how often brakes are applied, and other anomalies, the intelligent system can proactively recommend fixes and even schedule the maintenance. Almost any important component in a car can be monitored, including air system, power electronics, transmission, battery, exhaust gas system, fuel injection system, brakes, filter, oil pump, belts and chains, hydraulics, and many more.
Predictive maintenance helps keep the vehicle safer and also can lower costs by proactively addressing issues before they get too hard to fix. It prevents unexpected breakdowns and helps owners understand the future condition and value of their cars. By combining maintenance needs for multiple components, for example, drivers can reduce the number of shop visits and save significant time and money.
Marketing Opportunities for Connected Cars
By 2030, about 95 percent of new vehicles sold globally will be connected, up from around 50 percent today, according to McKinsey, and a single car can generate one to two terabytes of raw data each day. This presents a huge marketing opportunity. It’s all about big data analytics — collecting volumes of driver preferences and behavioral information to make buying recommendations and improving the customer experience. Examples include:
- Collecting, driving habits, entertainment and music, identity information, and how occupants use applications – all to be used for personalized marketing.
- Monitoring driver consumption of features like navigation and theft protection to sell additional related services.
- Enabling speech-controlled messaging within the car for emails, calendars and virtual assistants.
- Lowering costs by optimizing warranties based on onboard product data and consumption.
Several big technology industry players are now getting into the autonomous driving and connected car market in different ways:
Alphabet: Waymo has evolved into the leading autonomous driving tech venture in the US, now valued at $105 billion and expected to own 18 percent of the AV market by 2030.
Apple: Apple’s Project Titan has the goal of developing an autonomous electric vehicle and has been in development for the last few years. Apple also has CarPlay, a software solution for bringing its operating systems into cars, now available in 500 car models.
Amazon: Amazon is driving to capture the in-car experience with its Alexa platform for voice-assisted car instructions, and the company has also invested in self-driving technology and electrification.
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IoT and ML Will Drive Autonomous Driving and Connected Cars
There is a massive market on the horizon for connected vehicles, and technology professionals will want to understand both the mechanics of IoT devices and the inner workings of machine learning to excel in this exciting field.
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